Decision Support Systems

The need for company executives to make better decisions and the rapid evolution of computing power led to the birth of decision support systems (DSSs). A DSS is a type of computer information system whose purpose is to support decision making processes. A well-designed DSS is an interactive software system that helps decision makers aggregate useful information from raw data, documents, and business models to solve problems and make decisions.

While these systems were first implemented in executive circles, they have quickly grown to be used by trained professionals as well. Various remnants of DSS software implementations can be found everywhere from the Internet to your local bank branch. For example, when you go to a bank and apply for a loan, complex DSS software is used to determine the risk to the bank based on your financial history. The result of this information will aid the loan officer as to whether the bank should make the decision to loan you money.

DSSs gained tremendous popularity in the late ’80s and early ’90s. The first systems that were deployed targeted large-scale organizations that needed help with large amounts of data which included the government, and the automobile and health care industries. These systems were very successful and delivered tremendous return on investment.

Early DSS projects, while largely successful, did have some challenges however:

Customizability: DSS software did not exist in the way it does today. A vendor couldn’t simply download a tool or customize a preexisting system. Usually, these tools had to be designed and programmed from scratch.

Multiple vendors: Implementations of early DSSs were a mix of software, hardware, servers, networking, and back-end services. In the ’80s and early ’90s, there wasn’t a single company that could provide all of the necessary components of complex systems at once. Multiple vendors usually worked on a single project together on a single DSS implementation.

Uniqueness: Early DSS software was unique and often the first of its kind. This usually meant that a great deal of planning had to be done to get concepts moved from theory into a working information system. Architects and programmers in the early days of DSS couldn’t rely on how-to guides to implement a unique custom system.

Long deployments: Projects that included custom software and hardware from multiple vendors obviously led to implementations that took a long time to complete.

Expensiveness: DSS systems in the ’80s and ’90s were very expensive and easily carried budgets of tens of millions of dollars.

DSSs allowed for entire organizations to function more effectively, as the underlying software powering those organizations provided insights from large amounts of data. This aided human decision makers to apply data models into their own decision making processes.

DSS software at its start was considered a luxury, as only the largest of organizations could afford its power. Since the software was custom and worked with the cooperation of multiple vendors, it was hard to apply these systems as reusable and resalable deployments. Tens of thousands of hours were invested in making these systems come to life. In the process of designing these complex systems, many innovations and great strides were made in the young software industry. These innovations were screaming to be let out into the wild and used in conjunction with other pieces of software.

The demand for DSS software was ripe and the vendors were beginning to taste the huge amounts of potential profits. If only they could make the software a little more generic and resalable, they could start selling smaller DSS implementations to a much larger audience. This idea led to applying the core innovations of complex DSS software into many smaller principles like data mining, data aggregation, enterprise reporting, and dimensional analysis. Enterprise software vendors started delivering pieces of DSS as separate application packages, and the early seeds of BI were sown.